generating haiku with word association normskfirbar/dh/gaiku-yael.pdfwans word association norms -...
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Gaiku
Yael Kinor
Generating Haiku with Word Association NormsArticle by Yael Netzer, David Gabay, Yoav Goldberg, Michael Elhadad
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Contents- Computational Creativity
- What is Haiku?
- Where to start?
- Algorithm
- Experiment
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Computational Creativity Model, simulate or replicate creativity using a computer
- Program capable of human-level creativity
- Better understand human creativity
- Enhance human creativity
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Computational Creativity Challenge -
Understanding and modeling vague knowledge (beautiful, touching, funny)
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Oulipo Ouvroir de littérature potentielle - “Workshop of Potential Literature"
- French writers and mathematicians- Founded in 1960- Create works using constraints
“seeking of new structures and patterns which may be used by writers in any way they enjoy."
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Oulipo Methods
- N+7- Lipogram- Combinatorics- Snowball
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Poetry Generation
Three properties -
- Meaningfulness
- Grammaticality
- Poeticness
How can we apply to Haiku?
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Haiku俳句
Short form of Japanese poetry from 16th century
- Present tense, no judgment words
- Beauty lies in the things left unsaid
- Read between the lines
- Minimalism
- Express anything and everything
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Haiku俳句
Follows three technical rules -
- Kireji = cutting word
- 17 on = syllables
- Kigo = seasonal reference
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Kireji Special category of words
- Sense of closure or pause
- Emotional flavour and emphasis
- Ka, kana, -keri, -ramu, -shi, -tsu, ya
- No exact equivalent in English (dash, ellipsis)
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On Phonology unit that determines syllable weight (μ)
- A long vowel is one syllable but two moras
(Aa, Ou, Ei)
- 17 moras - 3 lines of 5 / 7 / 5
We will talk about syllables
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Kigo A word or phrase associated with a particular season
- Categories - seasons, plants, animals, earth,
humanity, heavens
- Early/Mid/Late of Spring/Summer/Autumn/Winter
- Saijiki
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古池や蛙飛びこむ水の音
furu ike ya / kawazu tobi komu / mizu no oto
an old ponda frog jumps intothe sound of water
Basho - 17 cen.
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Haiku俳句 - Kireji = cutting word
- 17 on = syllables
- Kigo = seasonal reference
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Haiku俳句 To convey one's mood In seventeen
syllables Is very diffic[Haiku, defined by John Cooper Clarke]
We will use a less constraining definition..
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How to write a computer program to generate Haiku?
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Let’s ask Stack Overflow
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We Need Words
Lexical databases
- WordNet - Thesaurus
Predictable and limited..
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We Need Words
Human mind uses varied associations
- Paradigmatic - (doctor-nurse)
- Syntagmatic - (mash-potato)
- Emotional - (math-yuck)
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We Need Words
Creativity requires -
- Knowledge
- Imagination
- Filtering
Bad Examples -
Haiku generator 1
Haiku Generator 2
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WANs Word Association Norms
- 40 years of psychological research
- Typical associations evoked by trigger
- Stable across people, time, language
Excellent for Haiku!
University of South Florida Free Association Norms
- 5K cue words + 10K additional words
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Are Haiku more associative than news or prose?
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WANs
Modelling
WordNet
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WANs
Modelling
WAN graph
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WANs Basic definitions -
Shortest path distances -
asso_dist(w1, w2) , wordNet_dist(w1, w2)
If [ asso_dist(w1, w2) < 3 ] => asso_related(w1, w2) = 1If [ wordNet_dist(w1, w2) < 4 ] => wordNet_related(w1, w2) = 1
If [ w1, w2 in WAN ] => in_wan(w1, w2) = 1
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WANs
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WANs
Associativity Test
- 200 Haiku- 12 word sequences Project Gutenberg- 12 word sequences NANC newswire corpus ($$)
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Generating Haiku using WANs
5 stages of creative process -
- Theme selection
- Syntactic planning
- Content selection / semantic planning
- Filtered over-generation
- Re-ranking
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Algorithm
Dataset
- USF WAN - 15K words
- Haiku corpus - 3500 English Haikus
- Google N-grams - 1TB of data
- Project Gutenberg - Easier to POS tag
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Algorithm
Theme Selection
1. User supplied seed word
2. Consult WAN database -
● 8 x Random walks from seed, 3 steps
● Keep all resulting words
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Theme selection
Random walks -
association graph vs. WordNet graph
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Algorithm
Syntactic Planning
Training stage -
1. POS-tag each Haiku
2. Extract line pattern - “DT_the JJ NN”
3. Top 40 frequent line patterns
=> Total 120 patterns
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Algorithm
Syntactic Planning
Generate Haiku -
- Choose random pattern for 1st line
- Choose 2nd line
- Choose 3rd line
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Algorithm
Syntactic Planning
Result - 3 line Haiku skeleton -
- Num of words in each line
- POS tags of words
- Placement of function words
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Algorithm
Content Selection
For each Haiku line -
- Search match in datasets (Google, Project Gutenberg)
- Syntactic patterns
- 1st line to contain seed word
- 2nd and 3rd contain any theme word
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Algorithm
Syntactic Planning
NN NN
DET_a NN of NNS
PP_in DET_the NN
alligator peara handful of whitesin the spring
avocado peara kind of bootsin the fall
pear salada season of tearsin the summer
pear treea seasoning of spicesin the fall
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Algorithm
Over Generation
- Create all possible Haiku candidates!- Filter away Haikus with undesired properties
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Algorithm
Re-ranking
Heuristics -
Maximize number of 2nd degree associations
*This method chooses quantity over quality..
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Is computer generated poem good enough?
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fishing guidesboat in the backgrounda new trip
blossomlessbut not unlovedthe old magnolia
first date —the little pileof anchovies
a holy cowa carton of milkseeking a church
blind snakeson the wet grasstombstoned terror
iced over pondI skip a rockthe entire width
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Experiment Turing Test
Test of machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.
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Experiment First set (AUTO)
- 10 random Haikus from corpus- 15 computer generated Haikus -
- Human identify main words in first line- Use main word as seed - 2 main words => 2 haikus
Second set (SEL)
- 9 award-winning Haikus - 17 human-selected computer generated Haikus
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Experiment - 50 adult subjects
- “Human” / “Not Human”
- 1-5 grade
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Experiment
Results
(SEL) 66.7% correct (AUTO) 61.4% correct
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Experiment
AUTOResults
Cherry tree / poisonous flowers lie / blooming (72.2% human)
Spring bloom / showing / the sun’s pyre(63.8% human)
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Experiment
SEL Results
Early dew / the water contains / teaspoons of honey (77.2% human)
Space journey / musical instruments mythology / of similar drugs(9% human)
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Can a computer generated creation capture the beauty of
a flower?
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Future work
- Enrich information in WordNet
- New creative structures
- Assisting with SLI
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fishing guidesboat in the backgrounda new trip
blossomlessbut not unlovedthe old magnolia
first date —the little pileof anchovies
a holy cowa carton of milkseeking a church
blind snakeson the wet grasstombstoned terror
iced over pondI skip a rockthe entire width